How to code qualitative data: inductive, deductive and abductive approaches explained

Coding qualitative data means assigning labels to segments of text to mark what each segment is about. Inductive coding lets themes emerge from the data without a prior framework. Deductive coding applies a pre-existing framework to the data. Abductive coding moves between both — starting inductively, then refining through a theoretical lens. Skimle supports all three through its automatic thematic analysis (inductive), predefined categories (deductive), and inductive analysis (abductive) modes. Which approach you need depends on your research question and whether you are exploring or testing.

Coding is the step that most qualitative research guides explain too briefly. "Assign codes to your data," they say, and then skip ahead to theme development. But the coding approach you choose determines the type of analysis you can do, the type of claims you can make, and whether your methodology will survive peer review. This guide explains the real differences.

What coding is and why it matters

A code is a label applied to a segment of text that captures what that segment is about. "Onboarding confusion." "Price sensitivity." "Trust in the brand." "Regret about the decision." Each of these could be a code — a concise description of the meaning in a passage of text.

Coding serves two purposes. First, it compresses the data: instead of reading 400 pages of transcripts every time you want to understand your dataset, you can navigate by code. Second, it makes aggregation possible: to see everything participants said about onboarding, you retrieve all segments tagged with the "onboarding confusion" code — which might come from 15 different interviews.

The choice of coding approach is really a choice about where the analytical structure comes from: from the data, from theory, or from both.

Inductive coding: letting the data lead

In inductive coding, you come to the data without a predetermined set of codes. You read each passage and ask: what is this about? What would I need to remember about this later? You generate a code label that captures the meaning in the researcher's own words, without forcing it into an existing category.

Inductive coding is appropriate when:

You are doing exploratory research and genuinely do not know in advance what themes will emerge
You want to minimise the risk of imposing your expectations on the data
The research question is "what is going on here?" rather than "how much of X is there?"
You are following a reflexive thematic analysis methodology where the researcher's interpretive role is foregrounded

The limitation of pure inductive coding is that it produces a lot of codes. An inductive coding pass through 20 interviews might generate 80 or 100 initial codes, many of which turn out to be slight variations of each other. A second pass is needed to consolidate and develop themes — which is where the real interpretive work happens.

Skimle's inductive analysis mode supports this iterative process: the AI generates an initial category structure from your documents, and you refine, merge, split, and rename categories through an interactive interface until the structure captures your interpretation accurately.

Deductive coding: applying a framework

In deductive coding, you start with a set of pre-existing codes — drawn from theory, a prior study, a conceptual framework, or a practical taxonomy — and apply those codes to the data. Rather than asking "what is this about?", you ask "which of my predefined categories does this belong to?"

Deductive coding is appropriate when:

You are testing a specific hypothesis or conceptual model
You are replicating or extending a prior study and want comparable categories
You need to code large volumes of data consistently and quickly
You are working in a context where the categories are given — for example, applying a customer journey framework to interview data, or coding employee survey comments against a pre-existing competency model

The risk of pure deductive coding is that it misses what is in the data but not in your framework. If every passage must be forced into an existing category, the data that does not fit gets lost — and often that anomalous data is the most interesting.

Skimle's predefined categories mode lets you define the category structure before analysis and then let the AI assign insights to your categories. This is the right choice when you already know what you are looking for.

Abductive coding: the approach most research actually uses

Abductive coding is what most experienced qualitative researchers actually do, even if they do not call it that. You start with a rough deductive framework (the concepts you expect to be relevant based on your literature review or research question), use inductive coding to pick up what the framework misses, and then revise your framework based on what you found — iterating until the codes and the theory are in dialogue.

Charles Sanders Peirce's concept of abduction describes reasoning that moves from observation to the best explanation, then tests and refines that explanation. Applied to qualitative coding, it means: you have a theory, the data talks back, and you revise the theory.

This is the methodology that produces the most interesting research findings — not the confirmation of what you already believed, not a purely empirical list of observations, but a developed theoretical understanding that has been tested against real data.

In practice, abductive coding looks like:

Start with a conceptual framework derived from the literature
Code inductively on a subset of your data (3–5 transcripts) to see what emerges
Compare your inductive codes to your deductive framework — what fits, what does not, what is missing?
Revise your coding framework based on what you have learned
Apply the revised framework to the full dataset
Repeat as needed until the framework accounts for the data adequately

Open, axial and selective coding in grounded theory

If you are following a grounded theory methodology, you will encounter a different set of terms: open coding, axial coding, and selective coding. These map roughly onto phases of the inductive-to-abductive continuum.

Open coding is the first-pass inductive reading — generating descriptive codes for everything in the data without yet worrying about how they relate.

Axial coding is the process of identifying relationships between codes — which codes cluster together? Which is cause and which is consequence? Which codes are dimensions of a higher-order concept?

Selective coding is the final stage, where you identify the core category — the central phenomenon around which the other categories organise — and build a theoretical model around it.

Grounded theory is a significant methodological commitment and typically not the right choice for applied research in business or HR contexts. But the three-stage coding progression (open → relational → selective) is useful as a mental model even outside formal grounded theory.

Practical tips that apply regardless of approach

Keep a codebook. A codebook is a document that defines each code: its name, its definition, an example of a passage that does and does not belong to it, and any codes it is related to. A good codebook makes your analysis consistent across time and across researchers. It is also the evidence that your coding is systematic rather than impressionistic.

Code iteratively, not in one pass. Your first coding pass is rough. Plan for at least two passes: the first to generate codes, the second to review, consolidate, and sharpen them. Some researchers do three or four passes on complex datasets.

Memo as you go. A memo is a note to yourself about an analytical decision or observation. "I am coding this as 'decision avoidance' rather than 'risk aversion' because the participant clearly knows what the right decision is but is not making it — that distinction seems important." Memos are the record of your interpretive reasoning. They are essential for reflexivity and for audit trails.

Distinguish descriptive from interpretive codes. Descriptive codes say what happened: "mentions pricing concerns." Interpretive codes say what it means: "price as proxy for trust in a new vendor." Both have a place, but the interpretive codes are where analysis actually lives.

How AI tools fit into the coding process

AI-assisted coding tools — including Skimle — change the speed and scale of the mechanical parts of coding without changing what rigorous analysis requires.

What AI tools do well: generating an initial code structure from large volumes of text, consistently applying codes to new passages once the framework is defined, surfacing patterns across a large corpus that would be invisible to manual reading.

What AI tools do not replace: the interpretive judgements that define good qualitative analysis. Which distinction matters? What is this pattern actually telling us? How do these codes relate to each other theoretically? Does this finding challenge or extend the framework I started with?

The guide to using AI in academic qualitative research covers methodological legitimacy in more detail, including how to document AI-assisted coding for publication.

The thematic analysis complete guide covers the full journey from coding to theme development to write-up.

Ready to try AI-assisted coding on your own dataset? Start with Skimle for free and run an inductive analysis on your transcripts — then compare the AI-generated structure to what you would have coded manually.

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About the author

Olli Salo is a former Partner at McKinsey & Company where he spent 18 years helping clients understand their markets, develop winning strategies and improve their operating models. He has done over 1000 client interviews and published over 10 articles on McKinsey.com and beyond. LinkedIn profile